The Internet continues to provide access to a nearly endless supply of new content and websites, which will continue to grow exponentially for the foreseeable future. This content growth is problematic for destination sites, content owners, and consumers.
For destination sites, there is increased competition for acquiring and retaining consumers. Many consumers rely on several favorite destination sites and/or frequent use of one or more search engines to discover desired content. Thus, destination sites must continually produce and/or acquire relevant content and convincingly present such content to their consumers. Search engines can be effective and are popular among consumers, however, such search engines are an intermediate step between the consumer and their desired content.
For content owners, there is difficulty in distributing and monetizing their content to increasing numbers of sites and audiences. To maximize potential revenue and profit, content owners must reach as large of an online audience as possible. In some instances, content owners must establish direct relationships with other destination sites or use conventional media or content distributors. Establishing and maintaining such relationships can be time consuming and expensive, and not every possible audience segment may be reached at any given time.
For consumers, it is increasingly difficult to discover all content the consumer really wants. Typically, consumers must “bounce” or otherwise surf between known destination sites, search results pages, or engage in numerous searches to find content they want. For many consumers, finding relevant content can be time consuming.
Conventional systems and methods for providing content to website consumers have relied on a variety of technologies and approaches, which in many instances, have yielded less than successful and often times inconsistent results. Since certain Internet advertising companies pioneered particular areas of contextual and behavioral targeting of advertising, the Internet industry has continually debated which targeting approach is more valid as particular companies begin to leverage these techniques to better target and recommend website content to site visitors. The reality is one or multiple models may be appropriate depending on the industry or content being consumed, versus relying on one particular approach. Various websites continue to implement technologies that give consumers more choices on what items they should click on next. Example links from section labels such as “Most Popular Stories”, “People Who Read This Also Read This”, “Related Content”, or “Most Commented” are often used as a next step. One goal of targeting content is to better predict consumer preference and demand for content, and then provide consumers with content they will find more interesting. Conventional systems and methods described above have several drawbacks and limitations.
Conventional contextual targeting utilizes keyword frequency to find additional content that includes mentions of primary subjects in an article. If an article is written about “Bernie Madoff”, contextual targeting will locate more content on “Bernie Madoff” based on the number of times “Bernie Madoff” is mentioned in additional articles, and then recommend content containing his name. The more times “Bernie Madoff” is mentioned, the higher the relevancy score for the article. A typical news site may have, for instance, 20 to 30 prior articles about “Bernie Madoff”, so a conventional system may select certain articles based on relevancy and publish date (newer articles versus older). Direct measurement of prior time spent with “Bernie Madoff”-related content is not used in this approach to identify content that performs well within the news industry because direct measurement of all “Bernie Madoff”-related content articles may be needed, for example in a particular sample, identifying which of the 30 articles written about “Bernie Madoff”, performed in the top 25% for consumer time spent with this content.
Conventional behavioral targeting of content utilizes selected additional content that other users have read based on commonalities in a navigational path. One conventional system utilizes collaborative-type filtering to accomplish this with its product recommendations. For example, if 20 users navigate from webpage A to webpage B, webpage B will be recommended on webpage A more frequently because it is navigated to more frequently. While this works well for certain websites with a particular scale and catalog depth, one problem with this approach as it relates to news and article related content is that whatever content is posted on a home page or is marketed as “popular” may tend to get recommended by users more because most consumers or users may only click on links from the home page. Thus recommending what may be popular on a particular day may inhibit or otherwise prevent keeping consumers or users engaged with a broader set of article content.
Conventional web analytics provided by particular companies can utilize certain data collected from a single web site to determine which aspects of the website work towards their business objectives. For example, some entities measure which content categories receive the most clicks by consumers or users. In turn, website owners using a content management system can use this data or clickstream to manually identify, tag, and deliver content they think consumers or users want. However, tagging content is often a manual process and fraught with user error, and in some instances content can be mis-categorized. Certain types of conventional analytics and automated tagging technologies may analyze a website's content at the subject level, and provide those websites with new views of how their content performs in comparison with their industry to identify new content needs. While several entities focus on web measurement at the industry level, in most instances, these entities fail to provide industry data about the content within and across those websites.
Thus, conventional systems and methods focus either on website traffic statistics (at the site level), such as site rankings, the growth rate and consumer sentiment around specific keywords, which in some instances may not be useful or particularly relevant measures of consumer interest in or demand for specific content, or utilize a purely contextual or behavioral approach to target content to consumers. Therefore, a need exists for systems and methods for providing targeted content to a network user.
Furthermore, conventional processes for distributing content to consumers via a network can be time consuming and expensive since the content must be frequently accessed at a content provider's server or otherwise made available via webpages and websites that often times must be consistently maintained. In many instances, an editor or publisher must manually review content before it is distributed to consumers, which increases the cost and time to distribute the content. Occasionally, certain content may not be suitable for certain audiences, and the editor or publisher may impose his or her own judgment to determine the suitability of such content for the intended audience. Therefore, a need exists for systems and methods for curating content to provide to a network user.